3.4. Analysis of Carbon Accounting Result
To account for the carbon emissions of the DS, it is necessary to trace the carbon flow between nodes at each time period. In this study, the nodes in the DS are categorized into load nodes and generator nodes. Generator nodes inject carbon flow into the system, and load nodes consume carbon flow. Therefore, the sum of inject-carbon flow is equal to the sum of load-carbon flow. The status of EV nodes varies at different times, necessitating an analysis of the carbon flow distribution for each period.
Table 3 and
Table 4 show the CI and CEFR of the distribution network at the 6th and 12th time periods, respectively.
At 6:00 AM, EV1 and EV2 are connected to the grid, and being considered as loads, the carbon flow injection at this period is from the PV and node31. The system’s carbon emissions totals 7716.04 kg CO2. At 12:00 PM, EV1 and EV2 are not connected to the grid, while EV3, EV4, EV5, and EV6 discharge to the grid, thus being considered as generator. The system’s carbon emission is 12,219.62 kg CO2.
The CEFR distribution in the DS allows for the calculation of the hourly system carbon emissions and the cumulative carbon emissions within 24 h.
Figure 9 displays the real-time carbon emissions and the carbon emission variation curve for the DS before and after low-carbon scheduling. The results indicate that over the course of a day, the DS with low-carbon scheduling reduced carbon emissions by 12,034.45 kg CO
2 compared to the system without low-carbon scheduling.
During the period from 0:00 to 9:00, the carbon emission factor of the grid is relatively low. However, from 10:00 to 23:00, the carbon emission factor is higher, leading to higher system carbon emissions. During peak periods of carbon emission factor, the system’s hourly carbon emissions after low-carbon scheduling are generally lower than those of the unscheduled system. The largest difference occurs at 18:00, where the carbon emissions of the system after low-carbon scheduling are reduced by 6888.49 kg CO2. For periods with lower carbon emission factor, the hourly carbon emissions of the system after low-carbon scheduling are generally higher than those of the unscheduled system. The largest difference occurs at 5:00, where the carbon emissions of the system after low-carbon scheduling are 3751.68 kg CO2 higher than the unscheduled system.
Therefore, from the perspective of the DS, it can be observed that EVs, through V2G scheduling, reduce the carbon emissions of the system during periods of high carbon emission factor. They shift charging demands periods of low-carbon electricity, effectively utilizing the fluctuating nature of grid carbon emission factor. This smoothens the carbon emission variation curve of the DS and reduces the total carbon emissions of the system within 24 h.
For EVs, V2G scheduling not only reduces the carbon emissions of the DS but also has an impact on the carbon emissions of EVs themselves.
Figure 10 compares the carbon emissions of six different EVs before and after low-carbon scheduling. Among them, EV1 and EV2 showed reductions of 680.90 kg CO
2 and 179.72 kg CO
2, respectively, after optimization scheduling. However, EV3, EV4, EV5, and EV6 did not show emission reductions. This variation is attributed to factors such as the timing of EV connection to the grid, their charging demands, and changes in their own CI.
Figure 11 illustrates the real-time variations in the CI of six types of EVs as well as the NCI of upstream nodes, to analyze the relationship between EV carbon emissions and CI.
Figure 11a,b depicts the changes in CI and their charging/discharging power for EV1 and EV2. During the 18:00–22:00 period, the CI of EVs is lower than the NCI of upstream nodes, and both EV1 and EV2 discharge to the grid to reduce the carbon emissions of the upstream node’s base load. From 1:00 to 9:00, the NCI of upstream nodes is lower than that of EVs, and both EV1 and EV2 primarily charge during this period to reduce the carbon emissions associated with EV charging. Additionally, the CI of EVs gradually decreases during the charging process, reaching values of 0.4383 kg CO
2/kWh and 0.4379 kg CO
2/kWh when leaving the grid, showing a reduction compared to when they were connected. EV3, EV4, EV5, and EV6 connect to the grid during periods of high carbon emission factor, as shown in
Figure 11c–f. When the NCI of upstream nodes is high, these EVs discharge to the grid to reduce the system’s carbon emissions. Conversely, when the NCI of upstream nodes is low, the EVs charge to reduce the carbon emissions associated with EV charging. Compared to when they first connect to the grid, the CI of EV3, EV5, and EV6 have all increased, while the EV4 upstream nodes have PV power injection during the 7:00–8:00 and 13:00–14:00 periods, reducing the NCI between upstream nodes and EV4. However, due to EV4 discharging low-carbon power to the system’s base load, it reduces the system’s carbon emissions.
To meet charging demands, high CI power is charged during 19:00–21:00. Consequently, although the CI of EV4 has decreased compared to pre-optimization, its carbon emissions have still increased. From this, we can infer that the CI of EVs can indirectly reflect the carbon emissions during the EV charging process. However, to accurately account for EV carbon reduction contribution to the distribution network, it is necessary to consider the combined effects of changes in the carbon reduction of DS and CI of EVs.
3.5. Result of Revenue Distribution
To ensure the long-term and stable participation of EV owners in low-carbon dispatch and to allocate profits reasonably, the average carbon price in the 2022 Chinese carbon market, which is 58.07 RMB/t CO
2 [
47], was used in this study to calculate the carbon cost and carbon reduction revenue for EVs. Based on the improved Shapley model,
Table 5 shows the carbon reduction amount V(S), low carbon cost C(S), and carbon reduction revenue (φ) for each EV alliance combination.
In most cases, cooperative operation results in higher carbon reduction revenue compared to independent operation. Taking EV1 and EV2 as an example, when EV1 and EV2 operate independently, the EV1 carbon reduction revenue is RMB 60.2, and the EV2 carbon contribution is RMB 187.0. However, when EV1 and EV2 operate together, the alliance’s overall carbon reduction revenue is RMB 299.3. This means that compared to operating independently, cooperative operation results in an additional RMB 52.1 of carbon reduction revenue for the system, primarily due to a reduction in carbon costs by RMB 94.4. In such cases, EV1 and EV2 are more inclined to cooperate. When EVs form a large alliance, the system’s low carbon cost decreases by 83%, and the total revenue increases by 82.8%.
Based on the allocation results shown in
Table 6, the analysis reveals that EV1 has a high carbon reduction contribution, and its CI when leaving the grid has a significant decrease compared to when it connects to the grid. Therefore, it incurs a higher low-carbon cost. The carbon reduction revenue allocation for EV1 is RMB 103.02. EV2 also has a high carbon reduction contribution, and the CI of EV2 when leaving the grid is the lowest, implying that it incurs the highest low-carbon cost. After allocation, EV2 receives a carbon reduction revenue of RMB 219.41.
For EV3, EV4, EV5, and EV6, considering the carbon reduction contributions and the low carbon costs incurred by these four entities, the revenue allocation based on the improved Shapley value is as follows: RMB 15.03, RMB 15.97, RMB 9.13, and RMB 13.11, respectively.
The comparison between the conventional allocation method and the improved Shapley value method proposed in this paper is shown in
Figure 12. Under the conventional allocation method, EVs that charge overnight have longer grid connection times, and their time period includes the grid load valley period with lower carbon emissions. EV1 and EV2 use V2G scheduling to utilize low-carbon electricity to meet their charging needs, significantly reducing their carbon emissions. They can also provide power to the base load during high carbon emission factor periods in the distribution grid, resulting in higher carbon reduction. On the other hand, EV3, EV4, EV5, and EV6 connect to the grid during peak load periods with higher carbon emissions. Their V2G scheduling capabilities are limited, making it difficult to reduce their own carbon emissions while satisfying their charging needs. The improved Shapley value allocation method takes into account both the carbon reduction of EVs and the corresponding low carbon costs associated with changes in carbon emissions. It provides a more comprehensive evaluation of the carbon reduction contributions of different entities in the operating system, leading to a more reasonable carbon reduction revenue distribution.